Analytical, numerical and experimental investigations of mixing fluids in microchannel

This work presents theoretical analysis, numerical simulation, fabrication and test of a micromixer chip for mixing fluids in microchannel. A three-dimensional analytical model is developed using a different mathematical approach to study passive laminar mixing phenomena and predict concentration distribution in a microchannel. The analytical model is validated by comparing with experimental and simulation results. The process of mixing fluids in a microchannel is simulated by solving the continuity, momentum and mass diffusion equations. The simulation results are validated and then parametric studies are performed to investigate the effects of channel aspect ratio, Reynolds number and diffusion coefficient on the mixing performance. The micromixer chip is fabricated with patterned SU-8 photoresist as the microchannel layer on a PMMA substrate using a combination of photolithography and micro-milling. Experiments are performed with different mixing fluids and the results were compared with that obtained from the theoretical model and simulation results.     

A robust alignment-free fingerprint hashing algorithm based on minimum distance graphs

Abstraction of a fingerprint in the form of a hash can be used for secure authentication. The main challenge is in finding the right choice of features which remain relatively invariant to distortions such as rotation, translation and minutiae insertions and deletions, while at the same time capturing the diversity across users. In this paper, an alignment-free novel fingerprint hashing algorithm is proposed which uses a graph comprising of the inter-minutia minimum distance vectors originating from the core point as a feature set called the minimum distance graph. Matching of hashes has been implemented using a corresponding search algorithm. Based on the experiments conducted on the FVC2002-DB1a and FVC2002-DB2a databases, we obtained an equal error rate of 2.27%. The computational cost associated with our fingerprint hash generation and matching processes is relatively low, despite its success in capturing the minutia positional variations across users.     

Experimental Comparison Research on Active Vibration Control for Flexible Piezoelectric Manipulator Using Fuzzy Controller

Space manipulators are flexible structures. Vibration problem will be unavoidable due to motion or external disturbance excitation. Model based control methods will not maintain the required accuracy because of the existence of nonlinear factors and parameter uncertainties. To solve these problems, fuzzy logic control laws with different membership function groups are adopted to suppress vibrations of a flexible smart manipulator using collocated piezoelectric sensor/actuator pair. Also, dual-mode controllers combining fuzzy logic and proportional integral control are designed, for suppressing the lower amplitude vibration near the equilibrium point significantly. Experimental comparison research is conducted, using fuzzy control algorithms and the dual-mode controllers with different membership functions. The experimental results show that the adopted fuzzy control algorithms can substantially suppress the larger amplitude vibration; and the dual-mode controllers can also damp out the lower amplitude vibration significantly. The experimental results demonstrate that the proposed fuzzy controllers and dual-mode controllers can suppress vibration effectively, and the optimal placement is feasible.     

Integral criteria for optimal tuning of PI/PID controllers for integrating processes

Tuning formulas for PI/PID controllers for integrating processes are presented in this paper. The controller parameters are obtained by minimizing various integral performance index. Bacterial Foraging strategy, a new entrant to the family of evolutionary algorithms is used for minimization to avoid the local minima in the optimization procedure. A setpoint filter is used to reduce the large overshoot, and a significant improvement in control performance is obtained when compared to recently reported methods. Simulation results for an assumed perturbation in the plant delay are also given to illustrate the robustness of the proposed controller design method. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Incremental maximum margin clustering

This paper proposes incremental maximum margin clustering in which one data point at a time is examined to decide which cluster the new data point belongs. The proposed method adopts the off-line iterative maximum margin clustering method’s alternating optimization algorithm. Accurate online support vector regression is employed in the alternating optimization. To avoid premature convergence, a sequence of decremental unlearning and incremental learning steps is performed. The proposed method is experimentally argued to (i) be scalable and competitive on training time front when compared with iterative maximum margin clustering and (ii) achieve competitive cluster quality compared to the off-line counterpart.     

A study on the channel bonding in IoT networks: Requirements,applications, and challenges

The most well-known sort of remote Internet connection is wireless local area networks (WLANs) due to its unsophisticated operation and deployment. Subsequently, the quantity of gadgets getting to the Internet through WLANs, for example, PCs, cell phones, or wearables, is expanding radically at the equivalent time that applications' throughput necessities do. To provide wireless networks with supplementary spectral resources, the researchers are considering the aggregation of frequency spectrums in licensed, unlicensed, and shared access (SA) bands. Channel aggregation/channel bonding (CA/CB) techniques accumulate quite a few channels together as one channel for the purpose of achieving better bandwidth utilization. In this study, we focus on reliable CA/CB techniques in different wireless networks. CA/CB procedures are utilized for empowering higher information rates by transmitting in more extensive channels, accordingly expanding range proficiency with the assured secure channel for communication. We also discuss the spectral scarcity issues in today's wireless IoT network. This paper presents an extensive survey on CA/CB procedures and methods, issues and challenges, and open research areas related to IoT devices. We analyze the performance of channel CA/CB strategies in the different wireless networks too.     

A system for semantic segmentation of TV news broadcast videos

TV news channels present rich and complete experience of various events through audio-visual content. This makes television news an influential medium to affect masses and thus persuaded various social scientists and regulators to monitor and analyze the content of broadcast videos. An organized archive of newscast is a prerequisite for any such analysis. Creating such archive requires segmentation of continuous news videos into suitable logical units. Based on the application, these logical units may be one of channel content obtained after advertisement removal, different shows, news stories or video shots. In this work, we propose an end to end system with software architecture for segmenting the TV broadcast videos at all these four granularities. The videos are segmented into shots. Video shots are used as basic unit for all further processing. Video shots are first subjected to advertisement detection and removal to obtain the non-commercial channel content. This channel content is further processed to identify various program boundaries. We propose to identify three types of shows based on the presentation format viz. news bulletins, interviews and debates. News bulletins so obtained are processed further to obtain news stories. We propose a modular and scalable framework and software architecture for the broadcast segmentation system for deployment on a computation cluster. This involves scheduler based recording module and broadcast segmentation module. We have presented the detailed software architecture for individual modules, automation of entire processing pipeline along with resource and database management systems. We have implemented and verified the software architecture by deploying the proposed system on a cluster of nine desktops and one workstation. The deployed system was used for round the clock processing of three Indian English news channels.

     

A distance based clustering method for arbitrary shaped clusters in large datasets

Clustering has been widely used in different fields of science, technology, social science, etc. Naturally, clusters are in arbitrary (non-convex) shapes in a dataset. One important class of clustering is distance based method. However, distance based clustering methods usually find clusters of convex shapes. Classical single-link is a distance based clustering method, which can find arbitrary shaped clusters. It scans dataset multiple times and has time requirement of O(n²), where n is the size of the dataset. This is potentially a severe problem for a large dataset. In this paper, we propose a distance based clustering method, l-SL to find arbitrary shaped clusters in a large dataset. In this method, first leaders clustering method is applied to a dataset to derive a set of leaders; subsequently single-link method (with distance stopping criteria) is applied to the leaders set to obtain final clustering. The l-SL method produces a flat clustering. It is considerably faster than the single-link method applied to dataset directly. Clustering result of the l-SL may deviate nominally from final clustering of the single-link method (distance stopping criteria) applied to dataset directly. To compensate deviation of the l-SL, an improvement method is also proposed. Experiments are conducted with standard real world and synthetic datasets. Experimental results show the effectiveness of the proposed clustering methods for large datasets.     

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.